Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations25899
Missing cells229036
Missing cells (%)55.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory128.0 B

Variable types

Numeric16

Alerts

CALIPER is highly overall correlated with DEPTH and 1 other fieldsHigh correlation
DEPTH is highly overall correlated with CALIPER and 2 other fieldsHigh correlation
DT is highly overall correlated with CALIPER and 6 other fieldsHigh correlation
DTS is highly overall correlated with DT and 3 other fieldsHigh correlation
GR is highly overall correlated with POTA and 2 other fieldsHigh correlation
MSFR is highly overall correlated with NPHI and 3 other fieldsHigh correlation
NPHI is highly overall correlated with DT and 5 other fieldsHigh correlation
PEF is highly overall correlated with DEPTH and 1 other fieldsHigh correlation
POTA is highly overall correlated with GRHigh correlation
RHOB is highly overall correlated with DT and 4 other fieldsHigh correlation
RLLD is highly overall correlated with MSFR and 2 other fieldsHigh correlation
RLLS is highly overall correlated with DT and 3 other fieldsHigh correlation
SWE is highly overall correlated with DT and 2 other fieldsHigh correlation
THOR is highly overall correlated with GRHigh correlation
URAN is highly overall correlated with GR and 1 other fieldsHigh correlation
CALIPER has 441 (1.7%) missing values Missing
DRHO has 18157 (70.1%) missing values Missing
DT has 726 (2.8%) missing values Missing
DTS has 22211 (85.8%) missing values Missing
MSFR has 20072 (77.5%) missing values Missing
NPHI has 17655 (68.2%) missing values Missing
PEF has 18157 (70.1%) missing values Missing
POTA has 17789 (68.7%) missing values Missing
RHOB has 18157 (70.1%) missing values Missing
RLLD has 17892 (69.1%) missing values Missing
RLLS has 17884 (69.1%) missing values Missing
SWE has 24170 (93.3%) missing values Missing
THOR has 17789 (68.7%) missing values Missing
URAN has 17789 (68.7%) missing values Missing
MSFR is highly skewed (γ1 = 22.87353871) Skewed
RLLS is highly skewed (γ1 = 26.78765702) Skewed
DEPTH is uniformly distributed Uniform
DEPTH has unique values Unique

Reproduction

Analysis started2025-06-12 12:23:48.195534
Analysis finished2025-06-12 12:24:29.070862
Duration40.88 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

DEPTH
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct25899
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1645.625
Minimum27
Maximum3264.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:29.201269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile188.8625
Q1836.3125
median1645.625
Q32454.9375
95-th percentile3102.3875
Maximum3264.25
Range3237.25
Interquartile range (IQR)1618.625

Descriptive statistics

Standard deviation934.56771
Coefficient of variation (CV)0.56791049
Kurtosis-1.2
Mean1645.625
Median Absolute Deviation (MAD)809.375
Skewness0
Sum42620042
Variance873416.8
MonotonicityStrictly increasing
2025-06-12T15:54:29.402651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 1
 
< 0.1%
2184.75 1
 
< 0.1%
2186 1
 
< 0.1%
2185.875 1
 
< 0.1%
2185.75 1
 
< 0.1%
2185.625 1
 
< 0.1%
2185.5 1
 
< 0.1%
2185.375 1
 
< 0.1%
2185.25 1
 
< 0.1%
2185.125 1
 
< 0.1%
Other values (25889) 25889
> 99.9%
ValueCountFrequency (%)
27 1
< 0.1%
27.125 1
< 0.1%
27.25 1
< 0.1%
27.375 1
< 0.1%
27.5 1
< 0.1%
27.625 1
< 0.1%
27.75 1
< 0.1%
27.875 1
< 0.1%
28 1
< 0.1%
28.125 1
< 0.1%
ValueCountFrequency (%)
3264.25 1
< 0.1%
3264.125 1
< 0.1%
3264 1
< 0.1%
3263.875 1
< 0.1%
3263.75 1
< 0.1%
3263.625 1
< 0.1%
3263.5 1
< 0.1%
3263.375 1
< 0.1%
3263.25 1
< 0.1%
3263.125 1
< 0.1%

CALIPER
Real number (ℝ)

High correlation  Missing 

Distinct20256
Distinct (%)79.6%
Missing441
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean14.212401
Minimum7.6933
Maximum21.8163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:29.597690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.6933
5-th percentile8.28717
Q19.03405
median12.8319
Q318.367575
95-th percentile21.533715
Maximum21.8163
Range14.123
Interquartile range (IQR)9.333525

Descriptive statistics

Standard deviation4.6404477
Coefficient of variation (CV)0.32650694
Kurtosis-1.4502568
Mean14.212401
Median Absolute Deviation (MAD)4.41775
Skewness0.13449912
Sum361819.31
Variance21.533754
MonotonicityNot monotonic
2025-06-12T15:54:29.798409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.2515 9
 
< 0.1%
8.3397 9
 
< 0.1%
12.362 8
 
< 0.1%
8.2475 8
 
< 0.1%
8.3767 8
 
< 0.1%
8.3782 8
 
< 0.1%
8.3428 8
 
< 0.1%
8.5965 8
 
< 0.1%
8.2497 8
 
< 0.1%
12.3386 7
 
< 0.1%
Other values (20246) 25377
98.0%
(Missing) 441
 
1.7%
ValueCountFrequency (%)
7.6933 1
< 0.1%
7.6945 1
< 0.1%
7.6948 2
< 0.1%
7.6949 1
< 0.1%
7.6961 1
< 0.1%
7.6963 1
< 0.1%
7.6966 1
< 0.1%
7.6967 1
< 0.1%
7.6969 1
< 0.1%
7.6974 1
< 0.1%
ValueCountFrequency (%)
21.8163 1
< 0.1%
21.8147 1
< 0.1%
21.8144 1
< 0.1%
21.8142 1
< 0.1%
21.8131 1
< 0.1%
21.8115 1
< 0.1%
21.8106 1
< 0.1%
21.8081 1
< 0.1%
21.808 1
< 0.1%
21.8054 1
< 0.1%

DRHO
Real number (ℝ)

Missing 

Distinct6758
Distinct (%)87.3%
Missing18157
Missing (%)70.1%
Infinite0
Infinite (%)0.0%
Mean0.079706794
Minimum-1.313353
Maximum0.680282
Zeros1
Zeros (%)< 0.1%
Negative189
Negative (%)0.7%
Memory size202.5 KiB
2025-06-12T15:54:30.019136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.313353
5-th percentile0.01126695
Q10.024
median0.0386555
Q30.11526425
95-th percentile0.302672
Maximum0.680282
Range1.993635
Interquartile range (IQR)0.09126425

Descriptive statistics

Standard deviation0.14511561
Coefficient of variation (CV)1.8206178
Kurtosis33.375267
Mean0.079706794
Median Absolute Deviation (MAD)0.0195625
Skewness-3.044913
Sum617.09
Variance0.021058539
MonotonicityNot monotonic
2025-06-12T15:54:30.229585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.025 20
 
0.1%
0.028 19
 
0.1%
0.031 18
 
0.1%
0.026 17
 
0.1%
0.029 16
 
0.1%
0.04 14
 
0.1%
0.03 14
 
0.1%
0.024 14
 
0.1%
0.033 13
 
0.1%
0.032 12
 
< 0.1%
Other values (6748) 7585
29.3%
(Missing) 18157
70.1%
ValueCountFrequency (%)
-1.313353 1
< 0.1%
-1.291451 1
< 0.1%
-1.264108 1
< 0.1%
-1.263648 1
< 0.1%
-1.261351 1
< 0.1%
-1.258502 1
< 0.1%
-1.254021 1
< 0.1%
-1.252644 1
< 0.1%
-1.2416 1
< 0.1%
-1.233487 1
< 0.1%
ValueCountFrequency (%)
0.680282 1
< 0.1%
0.666841 1
< 0.1%
0.661303 1
< 0.1%
0.658664 1
< 0.1%
0.651719 1
< 0.1%
0.650068 1
< 0.1%
0.649453 1
< 0.1%
0.645822 1
< 0.1%
0.643585 1
< 0.1%
0.642082 1
< 0.1%

DT
Real number (ℝ)

High correlation  Missing 

Distinct24710
Distinct (%)98.2%
Missing726
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean90.548042
Minimum37.9982
Maximum205.452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:30.448739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum37.9982
5-th percentile55.50612
Q167.309
median86.7352
Q3109.6085
95-th percentile138.97334
Maximum205.452
Range167.4538
Interquartile range (IQR)42.2995

Descriptive statistics

Standard deviation26.977269
Coefficient of variation (CV)0.29793321
Kurtosis-0.072710058
Mean90.548042
Median Absolute Deviation (MAD)20.5663
Skewness0.66502351
Sum2279365.9
Variance727.77304
MonotonicityNot monotonic
2025-06-12T15:54:30.648164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.0508 3
 
< 0.1%
84.9984 3
 
< 0.1%
58.8886 3
 
< 0.1%
106.3022 3
 
< 0.1%
64.44 3
 
< 0.1%
91.8703 3
 
< 0.1%
73.722 3
 
< 0.1%
57.276 3
 
< 0.1%
81.709 2
 
< 0.1%
83.7495 2
 
< 0.1%
Other values (24700) 25145
97.1%
(Missing) 726
 
2.8%
ValueCountFrequency (%)
37.9982 1
< 0.1%
40.0396 1
< 0.1%
41.712 1
< 0.1%
41.9086 1
< 0.1%
42.1962 1
< 0.1%
43.913 1
< 0.1%
44.417 1
< 0.1%
44.5887 1
< 0.1%
44.7132 1
< 0.1%
45.333 1
< 0.1%
ValueCountFrequency (%)
205.452 1
< 0.1%
204.0099 1
< 0.1%
203.5056 1
< 0.1%
203.235 1
< 0.1%
202.7277 1
< 0.1%
202.4049 1
< 0.1%
201.9583 1
< 0.1%
199.8873 1
< 0.1%
197.9677 1
< 0.1%
197.1873 1
< 0.1%

DTS
Real number (ℝ)

High correlation  Missing 

Distinct3445
Distinct (%)93.4%
Missing22211
Missing (%)85.8%
Infinite0
Infinite (%)0.0%
Mean127.69546
Minimum78.2348
Maximum258.1813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:30.840654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum78.2348
5-th percentile104.67996
Q1117.55327
median125.5689
Q3133.16142
95-th percentile169.36866
Maximum258.1813
Range179.9465
Interquartile range (IQR)15.60815

Descriptive statistics

Standard deviation18.901311
Coefficient of variation (CV)0.14801866
Kurtosis8.1424035
Mean127.69546
Median Absolute Deviation (MAD)7.74855
Skewness2.129083
Sum470940.86
Variance357.25955
MonotonicityNot monotonic
2025-06-12T15:54:31.042094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.373 17
 
0.1%
121.8387 12
 
< 0.1%
118.7692 12
 
< 0.1%
127.1415 10
 
< 0.1%
134.9526 10
 
< 0.1%
131.8831 9
 
< 0.1%
131.3257 9
 
< 0.1%
94.7747 9
 
< 0.1%
132.1637 8
 
< 0.1%
119.3285 8
 
< 0.1%
Other values (3435) 3584
 
13.8%
(Missing) 22211
85.8%
ValueCountFrequency (%)
78.2348 1
< 0.1%
83.156 1
< 0.1%
85.1544 1
< 0.1%
91.4987 1
< 0.1%
91.8475 1
< 0.1%
92.3018 1
< 0.1%
92.4819 1
< 0.1%
92.9119 1
< 0.1%
93.056 1
< 0.1%
93.3111 1
< 0.1%
ValueCountFrequency (%)
258.1813 1
< 0.1%
257.0172 1
< 0.1%
252.5105 1
< 0.1%
251.09 1
< 0.1%
246.8397 1
< 0.1%
245.1627 1
< 0.1%
241.7732 1
< 0.1%
241.169 1
< 0.1%
239.2354 1
< 0.1%
235.4982 1
< 0.1%

GR
Real number (ℝ)

High correlation 

Distinct24736
Distinct (%)96.1%
Missing147
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean31.114276
Minimum4.7012
Maximum169.5656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:31.257205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.7012
5-th percentile12.84083
Q123.512075
median31.0855
Q336.66155
95-th percentile53.4755
Maximum169.5656
Range164.8644
Interquartile range (IQR)13.149475

Descriptive statistics

Standard deviation12.340763
Coefficient of variation (CV)0.39662704
Kurtosis5.7585259
Mean31.114276
Median Absolute Deviation (MAD)6.5079
Skewness1.3171473
Sum801254.83
Variance152.29443
MonotonicityNot monotonic
2025-06-12T15:54:31.458155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.7185 3
 
< 0.1%
30.2334 3
 
< 0.1%
22.4525 3
 
< 0.1%
23.5394 3
 
< 0.1%
33.7976 3
 
< 0.1%
37.147 3
 
< 0.1%
23.2878 3
 
< 0.1%
34.0674 3
 
< 0.1%
26.8269 3
 
< 0.1%
30.3239 3
 
< 0.1%
Other values (24726) 25722
99.3%
(Missing) 147
 
0.6%
ValueCountFrequency (%)
4.7012 1
< 0.1%
4.7229 1
< 0.1%
4.7604 1
< 0.1%
4.7688 1
< 0.1%
4.8401 1
< 0.1%
4.9034 1
< 0.1%
4.9324 1
< 0.1%
4.9413 1
< 0.1%
5.0114 1
< 0.1%
5.0563 1
< 0.1%
ValueCountFrequency (%)
169.5656 1
< 0.1%
168.8787 1
< 0.1%
154.4848 1
< 0.1%
151.7542 1
< 0.1%
146.074 1
< 0.1%
144.559 1
< 0.1%
132.4453 1
< 0.1%
131.552 1
< 0.1%
125.05 1
< 0.1%
124.777 1
< 0.1%

MSFR
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct5557
Distinct (%)95.4%
Missing20072
Missing (%)77.5%
Infinite0
Infinite (%)0.0%
Mean3.041833
Minimum0.293
Maximum150.617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:31.674062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.293
5-th percentile0.62564
Q11.3675
median2.5446
Q34.1247
95-th percentile6.37341
Maximum150.617
Range150.324
Interquartile range (IQR)2.7572

Descriptive statistics

Standard deviation3.7380933
Coefficient of variation (CV)1.228895
Kurtosis791.07266
Mean3.041833
Median Absolute Deviation (MAD)1.3216
Skewness22.873539
Sum17724.761
Variance13.973342
MonotonicityNot monotonic
2025-06-12T15:54:31.884415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0439 3
 
< 0.1%
0.6262 3
 
< 0.1%
0.8094 3
 
< 0.1%
3.2628 3
 
< 0.1%
2.3679 3
 
< 0.1%
1.0426 3
 
< 0.1%
4.2551 2
 
< 0.1%
1.0404 2
 
< 0.1%
1.9909 2
 
< 0.1%
0.8349 2
 
< 0.1%
Other values (5547) 5801
 
22.4%
(Missing) 20072
77.5%
ValueCountFrequency (%)
0.293 1
< 0.1%
0.2993 1
< 0.1%
0.3014 1
< 0.1%
0.3078 1
< 0.1%
0.33 1
< 0.1%
0.3428 1
< 0.1%
0.3494 1
< 0.1%
0.3551 1
< 0.1%
0.3735 1
< 0.1%
0.3892 1
< 0.1%
ValueCountFrequency (%)
150.617 1
< 0.1%
139.5726 1
< 0.1%
87.3739 1
< 0.1%
70.4575 1
< 0.1%
45.8032 1
< 0.1%
41.1029 1
< 0.1%
38.2574 1
< 0.1%
33.3531 1
< 0.1%
32.5994 1
< 0.1%
21.7728 1
< 0.1%

NPHI
Real number (ℝ)

High correlation  Missing 

Distinct8227
Distinct (%)99.8%
Missing17655
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean0.1950709
Minimum-0.0025803
Maximum0.8761411
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size202.5 KiB
2025-06-12T15:54:32.104092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0025803
5-th percentile0.057492175
Q10.1231486
median0.1788142
Q30.2427924
95-th percentile0.39914951
Maximum0.8761411
Range0.8787214
Interquartile range (IQR)0.1196438

Descriptive statistics

Standard deviation0.10417111
Coefficient of variation (CV)0.53401667
Kurtosis2.8960889
Mean0.1950709
Median Absolute Deviation (MAD)0.05879765
Skewness1.2237263
Sum1608.1645
Variance0.010851621
MonotonicityNot monotonic
2025-06-12T15:54:32.306872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1212393 2
 
< 0.1%
0.2043327 2
 
< 0.1%
0.2553434 2
 
< 0.1%
0.1432945 2
 
< 0.1%
0.1757884 2
 
< 0.1%
0.0615728 2
 
< 0.1%
0.2115117 2
 
< 0.1%
0.251952 2
 
< 0.1%
0.3031459 2
 
< 0.1%
0.0999413 2
 
< 0.1%
Other values (8217) 8224
31.8%
(Missing) 17655
68.2%
ValueCountFrequency (%)
-0.0025803 1
< 0.1%
-0.0014899 1
< 0.1%
0.0005872 1
< 0.1%
0.0006554 1
< 0.1%
0.0009857 1
< 0.1%
0.001621 1
< 0.1%
0.0021496 1
< 0.1%
0.0035096 1
< 0.1%
0.004471 1
< 0.1%
0.0079826 1
< 0.1%
ValueCountFrequency (%)
0.8761411 1
< 0.1%
0.8694249 1
< 0.1%
0.863151 1
< 0.1%
0.8458993 1
< 0.1%
0.8421206 1
< 0.1%
0.8185784 1
< 0.1%
0.8062005 1
< 0.1%
0.7781732 1
< 0.1%
0.7548624 1
< 0.1%
0.7492355 1
< 0.1%

PEF
Real number (ℝ)

High correlation  Missing 

Distinct7114
Distinct (%)91.9%
Missing18157
Missing (%)70.1%
Infinite0
Infinite (%)0.0%
Mean6.7212178
Minimum1.9523
Maximum51.1694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:32.507217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.9523
5-th percentile2.745115
Q13.597025
median4.4839
Q35.358875
95-th percentile21.92378
Maximum51.1694
Range49.2171
Interquartile range (IQR)1.76185

Descriptive statistics

Standard deviation6.4410283
Coefficient of variation (CV)0.9583127
Kurtosis8.9934782
Mean6.7212178
Median Absolute Deviation (MAD)0.8858
Skewness2.8553904
Sum52035.668
Variance41.486846
MonotonicityNot monotonic
2025-06-12T15:54:33.117921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0303 4
 
< 0.1%
4.2197 4
 
< 0.1%
3.253 4
 
< 0.1%
4.7524 4
 
< 0.1%
4.9971 3
 
< 0.1%
4.6979 3
 
< 0.1%
4.8317 3
 
< 0.1%
3.941 3
 
< 0.1%
4.7531 3
 
< 0.1%
4.764 3
 
< 0.1%
Other values (7104) 7708
29.8%
(Missing) 18157
70.1%
ValueCountFrequency (%)
1.9523 1
< 0.1%
1.9524 1
< 0.1%
1.9578 1
< 0.1%
1.9605 1
< 0.1%
1.9861 1
< 0.1%
2.0021 1
< 0.1%
2.0562 1
< 0.1%
2.0974 1
< 0.1%
2.1031 1
< 0.1%
2.1042 1
< 0.1%
ValueCountFrequency (%)
51.1694 1
< 0.1%
50.4787 1
< 0.1%
49.9647 1
< 0.1%
48.5224 1
< 0.1%
47.4308 1
< 0.1%
46.6074 1
< 0.1%
45.7 1
< 0.1%
45.1052 1
< 0.1%
44.4951 1
< 0.1%
44.446 1
< 0.1%

POTA
Real number (ℝ)

High correlation  Missing 

Distinct5894
Distinct (%)72.7%
Missing17789
Missing (%)68.7%
Infinite0
Infinite (%)0.0%
Mean0.74608231
Minimum0.0126
Maximum3.2214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:33.314454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0126
5-th percentile0.214845
Q10.52025
median0.7412
Q30.949775
95-th percentile1.293475
Maximum3.2214
Range3.2088
Interquartile range (IQR)0.429525

Descriptive statistics

Standard deviation0.3450813
Coefficient of variation (CV)0.46252444
Kurtosis2.3134986
Mean0.74608231
Median Absolute Deviation (MAD)0.2128
Skewness0.69955316
Sum6050.7275
Variance0.1190811
MonotonicityNot monotonic
2025-06-12T15:54:33.519296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6623 7
 
< 0.1%
0.7565 5
 
< 0.1%
0.9626 5
 
< 0.1%
0.2937 5
 
< 0.1%
0.6669 5
 
< 0.1%
0.8943 5
 
< 0.1%
0.6498 5
 
< 0.1%
0.6742 5
 
< 0.1%
0.704 5
 
< 0.1%
0.855 5
 
< 0.1%
Other values (5884) 8058
31.1%
(Missing) 17789
68.7%
ValueCountFrequency (%)
0.0126 1
< 0.1%
0.0127 1
< 0.1%
0.0148 1
< 0.1%
0.0149 1
< 0.1%
0.0177 1
< 0.1%
0.019 1
< 0.1%
0.0208 1
< 0.1%
0.0259 1
< 0.1%
0.0285 1
< 0.1%
0.0378 1
< 0.1%
ValueCountFrequency (%)
3.2214 1
< 0.1%
3.1803 1
< 0.1%
2.8743 1
< 0.1%
2.7944 1
< 0.1%
2.7516 1
< 0.1%
2.7393 1
< 0.1%
2.7219 1
< 0.1%
2.6492 1
< 0.1%
2.6052 1
< 0.1%
2.5139 1
< 0.1%

RHOB
Real number (ℝ)

High correlation  Missing 

Distinct1794
Distinct (%)23.2%
Missing18157
Missing (%)70.1%
Infinite0
Infinite (%)0.0%
Mean2.5244751
Minimum1.221
Maximum3.1098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:33.720544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.221
5-th percentile2.258905
Q12.465
median2.552
Q32.615
95-th percentile2.74338
Maximum3.1098
Range1.8888
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.19247717
Coefficient of variation (CV)0.076244431
Kurtosis17.825221
Mean2.5244751
Median Absolute Deviation (MAD)0.074
Skewness-3.2211662
Sum19544.486
Variance0.03704746
MonotonicityNot monotonic
2025-06-12T15:54:33.923470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.587 39
 
0.2%
2.611 38
 
0.1%
2.6 37
 
0.1%
2.61 37
 
0.1%
2.592 37
 
0.1%
2.597 36
 
0.1%
2.598 35
 
0.1%
2.568 34
 
0.1%
2.609 34
 
0.1%
2.607 32
 
0.1%
Other values (1784) 7383
28.5%
(Missing) 18157
70.1%
ValueCountFrequency (%)
1.221 1
 
< 0.1%
1.231 1
 
< 0.1%
1.235 1
 
< 0.1%
1.238 4
 
< 0.1%
1.239 8
< 0.1%
1.24 7
< 0.1%
1.241 14
0.1%
1.242 5
 
< 0.1%
1.243 1
 
< 0.1%
1.244 1
 
< 0.1%
ValueCountFrequency (%)
3.1098 1
< 0.1%
3.0704 1
< 0.1%
3.062 1
< 0.1%
2.988 1
< 0.1%
2.985 2
< 0.1%
2.984 1
< 0.1%
2.978 1
< 0.1%
2.975 1
< 0.1%
2.97 1
< 0.1%
2.969 1
< 0.1%

RLLD
Real number (ℝ)

High correlation  Missing 

Distinct6004
Distinct (%)75.0%
Missing17892
Missing (%)69.1%
Infinite0
Infinite (%)0.0%
Mean182.40681
Minimum0.011
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:34.139138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.011
5-th percentile1.2252
Q12.8295
median5.198
Q38.444
95-th percentile35.5863
Maximum40000
Range39999.989
Interquartile range (IQR)5.6145

Descriptive statistics

Standard deviation2292.0589
Coefficient of variation (CV)12.565643
Kurtosis233.44336
Mean182.40681
Median Absolute Deviation (MAD)2.65
Skewness14.921552
Sum1460531.3
Variance5253534
MonotonicityNot monotonic
2025-06-12T15:54:34.360968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 15
 
0.1%
1.451 6
 
< 0.1%
3.61 6
 
< 0.1%
25000 6
 
< 0.1%
2.871 5
 
< 0.1%
1.224 5
 
< 0.1%
5 5
 
< 0.1%
1.988 5
 
< 0.1%
1.482 5
 
< 0.1%
4.716 5
 
< 0.1%
Other values (5994) 7944
30.7%
(Missing) 17892
69.1%
ValueCountFrequency (%)
0.011 1
< 0.1%
0.019 1
< 0.1%
0.03 1
< 0.1%
0.033 1
< 0.1%
0.038 1
< 0.1%
0.049 1
< 0.1%
0.064 1
< 0.1%
0.076 1
< 0.1%
0.087 1
< 0.1%
0.101 1
< 0.1%
ValueCountFrequency (%)
40000 15
0.1%
38757.477 1
 
< 0.1%
38017.719 1
 
< 0.1%
35158.891 1
 
< 0.1%
34213.977 1
 
< 0.1%
33159.656 1
 
< 0.1%
30023.52 1
 
< 0.1%
28025.023 1
 
< 0.1%
26479.148 1
 
< 0.1%
25734.176 1
 
< 0.1%

RLLS
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct6017
Distinct (%)75.1%
Missing17884
Missing (%)69.1%
Infinite0
Infinite (%)0.0%
Mean43.692169
Minimum0.044
Maximum25000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:34.572760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.044
5-th percentile0.7004
Q12.4765
median5.022
Q38.6485
95-th percentile34.0221
Maximum25000
Range24999.956
Interquartile range (IQR)6.172

Descriptive statistics

Standard deviation805.20401
Coefficient of variation (CV)18.429023
Kurtosis766.91483
Mean43.692169
Median Absolute Deviation (MAD)2.941
Skewness26.787657
Sum350192.73
Variance648353.49
MonotonicityNot monotonic
2025-06-12T15:54:34.777649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.607 8
 
< 0.1%
0.597 7
 
< 0.1%
0.689 7
 
< 0.1%
6.163 6
 
< 0.1%
3.043 6
 
< 0.1%
2.303 6
 
< 0.1%
25000 6
 
< 0.1%
0.66 5
 
< 0.1%
0.682 5
 
< 0.1%
1.364 5
 
< 0.1%
Other values (6007) 7954
30.7%
(Missing) 17884
69.1%
ValueCountFrequency (%)
0.044 1
< 0.1%
0.046 1
< 0.1%
0.048 1
< 0.1%
0.05 1
< 0.1%
0.053 1
< 0.1%
0.058 1
< 0.1%
0.061 2
< 0.1%
0.064 1
< 0.1%
0.065 1
< 0.1%
0.066 1
< 0.1%
ValueCountFrequency (%)
25000 6
< 0.1%
16003.98 1
 
< 0.1%
16003.785 1
 
< 0.1%
16002.469 1
 
< 0.1%
14019.256 1
 
< 0.1%
9986.219 1
 
< 0.1%
8005.057 1
 
< 0.1%
8004.997 1
 
< 0.1%
8004.569 1
 
< 0.1%
8003.467 1
 
< 0.1%

SWE
Real number (ℝ)

High correlation  Missing 

Distinct1463
Distinct (%)84.6%
Missing24170
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.49185483
Minimum0
Maximum1
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:34.976561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12576
Q10.317
median0.474
Q30.6459
95-th percentile0.9999
Maximum1
Range1
Interquartile range (IQR)0.3289

Descriptive statistics

Standard deviation0.23795259
Coefficient of variation (CV)0.48378622
Kurtosis-0.43321494
Mean0.49185483
Median Absolute Deviation (MAD)0.165
Skewness0.33435256
Sum850.417
Variance0.056621435
MonotonicityNot monotonic
2025-06-12T15:54:35.180970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 64
 
0.2%
0.9999 25
 
0.1%
0 9
 
< 0.1%
0.9998 4
 
< 0.1%
0.311 3
 
< 0.1%
0.2816 3
 
< 0.1%
0.2577 3
 
< 0.1%
0.4464 3
 
< 0.1%
0.4156 3
 
< 0.1%
0.3216 3
 
< 0.1%
Other values (1453) 1609
 
6.2%
(Missing) 24170
93.3%
ValueCountFrequency (%)
0 9
< 0.1%
0.0003 1
 
< 0.1%
0.0064 1
 
< 0.1%
0.0204 1
 
< 0.1%
0.0314 1
 
< 0.1%
0.0394 1
 
< 0.1%
0.0403 1
 
< 0.1%
0.0434 1
 
< 0.1%
0.0448 1
 
< 0.1%
0.0451 1
 
< 0.1%
ValueCountFrequency (%)
1 64
0.2%
0.9999 25
 
0.1%
0.9998 4
 
< 0.1%
0.996 1
 
< 0.1%
0.9948 1
 
< 0.1%
0.986 1
 
< 0.1%
0.9857 1
 
< 0.1%
0.9842 2
 
< 0.1%
0.9775 1
 
< 0.1%
0.9763 1
 
< 0.1%

THOR
Real number (ℝ)

High correlation  Missing 

Distinct7036
Distinct (%)86.8%
Missing17789
Missing (%)68.7%
Infinite0
Infinite (%)0.0%
Mean1.9657489
Minimum0.0446
Maximum26.5319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:35.375468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0446
5-th percentile0.260535
Q10.505275
median1.0054
Q32.0654
95-th percentile7.717135
Maximum26.5319
Range26.4873
Interquartile range (IQR)1.560125

Descriptive statistics

Standard deviation2.572671
Coefficient of variation (CV)1.3087486
Kurtosis10.798753
Mean1.9657489
Median Absolute Deviation (MAD)0.58185
Skewness2.8644892
Sum15942.223
Variance6.618636
MonotonicityNot monotonic
2025-06-12T15:54:35.576854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4923 5
 
< 0.1%
0.7656 4
 
< 0.1%
0.8067 4
 
< 0.1%
0.4782 4
 
< 0.1%
0.4717 4
 
< 0.1%
0.2937 4
 
< 0.1%
0.4539 4
 
< 0.1%
0.4349 4
 
< 0.1%
0.5752 4
 
< 0.1%
0.6056 4
 
< 0.1%
Other values (7026) 8069
31.2%
(Missing) 17789
68.7%
ValueCountFrequency (%)
0.0446 1
< 0.1%
0.0476 1
< 0.1%
0.049 1
< 0.1%
0.0565 1
< 0.1%
0.0603 1
< 0.1%
0.0606 1
< 0.1%
0.0666 1
< 0.1%
0.0727 1
< 0.1%
0.0735 1
< 0.1%
0.0743 1
< 0.1%
ValueCountFrequency (%)
26.5319 1
< 0.1%
25.6092 1
< 0.1%
24.5023 1
< 0.1%
21.8962 1
< 0.1%
20.6005 1
< 0.1%
19.5322 1
< 0.1%
19.4374 1
< 0.1%
19.243 1
< 0.1%
19.2014 1
< 0.1%
18.6345 1
< 0.1%

URAN
Real number (ℝ)

High correlation  Missing 

Distinct7103
Distinct (%)87.6%
Missing17789
Missing (%)68.7%
Infinite0
Infinite (%)0.0%
Mean1.8412787
Minimum0.0019
Maximum19.9267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2025-06-12T15:54:35.778159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0019
5-th percentile0.107635
Q10.7559
median1.39665
Q32.266975
95-th percentile5.68975
Maximum19.9267
Range19.9248
Interquartile range (IQR)1.511075

Descriptive statistics

Standard deviation1.7624333
Coefficient of variation (CV)0.95717898
Kurtosis10.629162
Mean1.8412787
Median Absolute Deviation (MAD)0.71465
Skewness2.5295608
Sum14932.77
Variance3.1061711
MonotonicityNot monotonic
2025-06-12T15:54:35.992907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.43 5
 
< 0.1%
1.294 5
 
< 0.1%
1.668 4
 
< 0.1%
6.042 4
 
< 0.1%
1.136 4
 
< 0.1%
1.571 4
 
< 0.1%
2.435 4
 
< 0.1%
0.036 4
 
< 0.1%
1.205 4
 
< 0.1%
1.6481 4
 
< 0.1%
Other values (7093) 8068
31.2%
(Missing) 17789
68.7%
ValueCountFrequency (%)
0.0019 1
< 0.1%
0.002 1
< 0.1%
0.0025 1
< 0.1%
0.003 1
< 0.1%
0.0037 1
< 0.1%
0.004 1
< 0.1%
0.0045 1
< 0.1%
0.0046 2
< 0.1%
0.005 1
< 0.1%
0.0051 1
< 0.1%
ValueCountFrequency (%)
19.9267 1
< 0.1%
18.9074 1
< 0.1%
18.7635 1
< 0.1%
17.4396 2
< 0.1%
17.2623 1
< 0.1%
16.213 1
< 0.1%
13.6154 1
< 0.1%
13.4897 1
< 0.1%
13.2451 1
< 0.1%
13.124 1
< 0.1%

Interactions

2025-06-12T15:54:25.572381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:49.124873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:51.694384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:54.135244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:56.831374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:59.252365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:01.774312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:04.076925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:06.616579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:08.861050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:11.077111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:13.647327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:15.993099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:18.419111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:20.774230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:23.306945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:25.713692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:49.293817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:51.831547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:54.278456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:56.986338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:59.394390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:01.918070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:04.229775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:06.743407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:08.990560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:11.219372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:13.778273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:16.125532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:18.542140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:20.902669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:23.444646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:25.864557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:49.460139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:51.994096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:54.442208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:57.145616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:53:59.542282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:02.063375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:04.375511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:06.882465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:09.128849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:11.368473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:13.923581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:16.284781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:18.689452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-12T15:54:01.618040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:03.923584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:06.458244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:08.718732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:10.932626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:13.469110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:15.840241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:18.258220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:20.618619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:23.159189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-12T15:54:25.414089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-12T15:54:36.158005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CALIPERDEPTHDRHODTDTSGRMSFRNPHIPEFPOTARHOBRLLDRLLSSWETHORURAN
CALIPER1.000-0.8370.3760.671-0.0980.2480.3750.0310.3220.2330.1230.2250.2160.1370.2190.138
DEPTH-0.8371.0000.456-0.723-0.174-0.1040.265-0.1830.552-0.4450.0960.1950.2940.4720.3840.476
DRHO0.3760.4561.0000.014-0.0080.127-0.2060.0720.322-0.1990.064-0.0090.0380.1520.2440.262
DT0.671-0.7230.0141.0000.8440.404-0.4780.733-0.1540.340-0.642-0.455-0.508-0.6840.2940.082
DTS-0.098-0.174-0.0080.8441.0000.159-0.4230.525-0.1540.185-0.758-0.350-0.436-0.623-0.013-0.009
GR0.248-0.1040.1270.4040.1591.0000.1290.3710.2430.520-0.103-0.131-0.072-0.0130.5450.587
MSFR0.3750.265-0.206-0.478-0.4230.1291.000-0.6520.3040.1720.5620.7330.7790.1000.2840.116
NPHI0.031-0.1830.0720.7330.5250.371-0.6521.000-0.1330.236-0.577-0.585-0.645-0.4580.2080.140
PEF0.3220.5520.322-0.154-0.1540.2430.304-0.1331.000-0.2420.0690.1730.2650.4840.1960.561
POTA0.233-0.445-0.1990.3400.1850.5200.1720.236-0.2421.000-0.075-0.194-0.207-0.1060.210-0.135
RHOB0.1230.0960.064-0.642-0.758-0.1030.562-0.5770.069-0.0751.0000.3970.4660.663-0.009-0.036
RLLD0.2250.195-0.009-0.455-0.350-0.1310.733-0.5850.173-0.1940.3971.0000.929-0.2390.0950.072
RLLS0.2160.2940.038-0.508-0.436-0.0720.779-0.6450.265-0.2070.4660.9291.000-0.1590.1730.151
SWE0.1370.4720.152-0.684-0.623-0.0130.100-0.4580.484-0.1060.663-0.239-0.1591.000-0.1550.100
THOR0.2190.3840.2440.294-0.0130.5450.2840.2080.1960.210-0.0090.0950.173-0.1551.0000.227
URAN0.1380.4760.2620.082-0.0090.5870.1160.1400.561-0.135-0.0360.0720.1510.1000.2271.000

Missing values

2025-06-12T15:54:28.052032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-12T15:54:28.421107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-12T15:54:28.785026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DEPTHCALIPERDRHODTDTSGRMSFRNPHIPEFPOTARHOBRLLDRLLSSWETHORURAN
027.000NaNNaNNaNNaN13.4222NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
127.125NaNNaNNaNNaN14.2646NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
227.250NaNNaNNaNNaN14.7782NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
327.375NaNNaNNaNNaN14.8532NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
427.500NaNNaNNaNNaN14.5207NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
527.625NaNNaNNaNNaN13.9892NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
627.750NaNNaNNaNNaN13.6897NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
727.875NaNNaNNaNNaN13.6741NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
828.000NaNNaNNaNNaN13.7637NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
928.125NaNNaNNaNNaN14.1557NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
DEPTHCALIPERDRHODTDTSGRMSFRNPHIPEFPOTARHOBRLLDRLLSSWETHORURAN
258893263.1257.6977NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
258903263.2507.6976NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
258913263.3757.6969NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
258923263.5007.6948NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
258933263.6257.6948NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
258943263.7507.6966NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
258953263.8757.6987NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
258963264.0007.6993NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
258973264.1257.6984NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
258983264.2507.6981NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN